AI Product Design
Designing products whose behaviour is probabilistic — where the system can be confidently wrong and the interface has to account for it.
Conventional software is deterministic: the same input produces the same output, and a wrong result is a bug with a fix. An AI feature is not. It returns a plausible answer with a confidence the user cannot see, it varies between identical requests, and it fails in ways that look exactly like success. Every interface decision follows from that: what the system claims, how a person checks it, and what happens when it is wrong.
Three problems recur. Setting expectations — what the feature can and cannot do, stated before use rather than discovered through failure. Showing provenance — what the answer was based on, so it can be verified without redoing the work. And designing the correction path, which is where most AI features are thin: regenerating is not a correction, it is a second guess. Undo, edit, and a way to say why the output was wrong are what make the feature usable on real work.
Cost and latency are design constraints
A model call can take seconds and costs money per request. That rules out patterns that assume instant, free responses — live previews on every keystroke, speculative generation — and makes streaming, caching and human review design decisions rather than implementation details.
In practice
A contract-review tool surfaced flagged clauses with no source reference. Lawyers had to re-read the document to verify each flag, which took longer than reading it once themselves; use collapsed after a month. Adding a click-through to the exact paragraph with the matched text highlighted turned it into the tool's most-used feature.
Where teams get it wrong
- Presenting probabilistic output with the confidence of a deterministic result.
- No provenance, forcing the user to redo the work to check it.
- Regenerate as the only correction path.
- Interfaces designed around the happy path, where the model is right.
- Ignoring latency and per-request cost until after the interaction pattern is built.
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Related terms
All terms- Generative UIInterfaces assembled at runtime by a model, rather than laid out in advance by a designer.
- Prompt DesignDesigning what a product asks a model, and what it asks the user — the interface layer around a prompt, not just the wording of one.
- Human in the LoopKeeping a person in the decision path of an automated system — reviewing, approving or correcting before an action takes effect.
- Vibe CodingBuilding software by describing what you want to an AI model and steering the result, rather than writing the code yourself.